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AI and Machine Learning in Façade Performance Prediction
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Author(s): Ansuman Samal (Siksha O Anusandhan (Deemed to be) University, India), V. C. Vandana (Government First Grade College, Malur, India), P. Selvakumar (Department of Science and Humanities, Nehru Institute of Technology, Coimbatore, India), R. Rukmathan (Dr. NGP Arts and Science College, India), Vijay Uprikar (Datta Meghe Institute of Management Studies, Nagpur, India)and Ninad Rithe (Datta Meghe Institute of Management Studies, Nagpur, India)
Copyright: 2026
Pages: 30
Source title:
Principles, Materials, and Applications in Facade Engineering
Source Author(s)/Editor(s): Arkar Htet (Mullion Facade Engineering School, Myanmar)and Theingi Aung (Mullion Facade Engineering School, Myanmar)
DOI: 10.4018/979-8-3373-6023-2.ch007
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Abstract
The integration of Artificial Intelligence (AI) and Machine Learning (ML) in the built environment is transforming how buildings are designed, constructed, operated, and maintained, with far-reaching implications for sustainability, occupant comfort, operational efficiency, and cost-effectiveness. In the context of building performance, AI and ML act as pivotal technologies that enable data-driven decision-making and proactive control strategies. As buildings become more complex and demand greater energy efficiency and user responsiveness, traditional rule-based systems often fall short in handling the volume, velocity, and variety of data generated by modern sensors and control devices. AI and ML offer powerful alternatives, with the ability to learn from historical and real-time data, recognize patterns, forecast performance, and autonomously adapt to changing conditions. One of the most critical areas where AI and ML contribute to building performance is energy management and optimization.
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